Real-time formation fluid analysis using fluid characterization models with synthetic optical sensor inputs is sensitive to the quality of sensor data transformation from the downhole tool parameter space to the synthetic parameter space, and to the quality of multivariate input selection. In common practice, each sensor has its own sensor-based fluid characterization models and cross-space data transformation models. While fluid characterization models are calibrated in a synthetic database using virtual sensor responses on a large collection of global oil and fluid samples with known properties, cross-space data transformation models are usually trained on a small number of reference fluids with measured sensor responses as calibration inputs and simulated virtual sensor responses as calibration outputs.
In formation sampling and testing, data from a downhole optical sensor are routinely converted to variable inputs of fluid characterization models through cross-space data mapping. However, quality related issues on fluid property estimation might arise because of the narrow dynamic range of optical signals used with sensor-based calibration, and the limitation of applying data transformation algorithms derived from a small number of reference fluids to a larger number of formation fluids having greater variety and complexity. Uncertainty in cross-space data mapping reduces the compatibility of transformed data with calibration data in virtual sensor parameter space, and could result in non-negligible prediction error on fluid answer products. The disagreement of input selection with sensor-based nonlinear calibration on each fluid predictive model may also produce inconsistent output from the sensors on the same tool or different tools when tested on the same fluids, making real-time signal processing and data interpretation difficult.
In the figures, elements having the same or similar reference numeral have the same or similar functionality and description, unless stated otherwise.